SoroushVahidi/scidocs huggingface dataset:
Frequency split metal artifact reduction (FSMAR) in computed tomography.
(2012)

PURPOSE
The problem of metal artifact reduction (MAR) is almost as old as the clinical use of computed tomography itself. When metal implants are present in the field of measurement, severe artifacts degrade the image quality and the diagnostic value of CT images. Up to now, no generally accepted solution to this issue has been found. In this work, a method based on a new MAR concept is presented: frequency split metal artifact reduction (FSMAR). It ensures efficient reduction of metal artifacts at high image quality with enhanced preservation of details close to metal implants.


METHODS
FSMAR combines a raw data inpainting-based MAR method with an image-based frequency split approach. Many typical methods for metal artifact reduction are inpainting-based MAR methods and simply replace unreliable parts of the projection data, for example, by linear interpolation. Frequency split approaches were used in CT, for example, by combining two reconstruction methods in order to reduce cone-beam artifacts. FSMAR combines the high frequencies of an uncorrected image, where all available data were used for the reconstruction with the more reliable low frequencies of an image which was corrected with an inpainting-based MAR method. The algorithm is tested in combination with normalized metal artifact reduction (NMAR) and with a standard inpainting-based MAR approach. NMAR is a more sophisticated inpainting-based MAR method, which introduces less new artifacts which may result from interpolation errors. A quantitative evaluation was performed using the examples of a simulation of the XCAT phantom and a scan of a spine phantom. Further evaluation includes patients with different types of metal implants: hip prostheses, dental fillings, neurocoil, and spine fixation, which were scanned with a modern clinical dual source CT scanner.


RESULTS
FSMAR ensures sharp edges and a preservation of anatomical details which is in many cases better than after applying an inpainting-based MAR method only. In contrast to other MAR methods, FSMAR yields images without the usual blurring close to implants.


CONCLUSIONS
FSMAR should be used together with NMAR, a combination which ensures an accurate correction of both high and low frequencies. The algorithm is computationally inexpensive compared to iterative methods and methods with complex inpainting schemes. No parameters were chosen manually; it is ready for an application in clinical routine.
Tagging Urdu Sentences from English POS Taggers
(2017)

Being a global language, English has attracted a majority of researchers and academia to work on several Natural Language Processing (NLP) applications. The rest of the languages are not focused as much as English. Part-of-speech (POS) Tagging is a necessary component for several NLP applications. An accurate POS Tagger for a particular language is not easy to construct due to the diversity of that language. The global language English, POS Taggers are more focused and widely used by the researchers and academia for NLP processing. In this paper, an idea of reusing English POS Taggers for tagging non-English sentences is proposed. On exemplary basis, Urdu sentences are processed to tagged from 11 famous English POS Taggers. State-of-the-art English POS Taggers were explored from the literature, however, 11 famous POS Taggers were being input to Urdu sentences for tagging. A famous Google translator is used to translate the sentences across the languages. Data from twitter.com is extracted for evaluation perspective. Confusion matrix with kappa statistic is used to measure the accuracy of actual Vs predicted tagging. The two best English POS Taggers which tagged Urdu sentences were Stanford POS Tagger and MBSP POS Tagger with an accuracy of 96.4% and 95.7%, respectively. The system can be generalized for multilingual sentence tagging. Keywords—Standford part-of-speech (POS) tagger; Google translator; Urdu POS tagging; kappa statistic
IR sensitivity enhancement of CMOS Image Sensor with diffractive light trapping pixels
(2017)

We report on the IR sensitivity enhancement of back-illuminated CMOS Image Sensor (BI-CIS) with 2-dimensional diffractive inverted pyramid array structure (IPA) on crystalline silicon (c-Si) and deep trench isolation (DTI). FDTD simulations of semi-infinite thick c-Si having 2D IPAs on its surface whose pitches over 400 nm shows more than 30% improvement of light absorption at λ = 850 nm and the maximum enhancement of 43% with the 540 nm pitch at the wavelength is confirmed. A prototype BI-CIS sample with pixel size of 1.2 μm square containing 400 nm pitch IPAs shows 80% sensitivity enhancement at λ = 850 nm compared to the reference sample with flat surface. This is due to diffraction with the IPA and total reflection at the pixel boundary. The NIR images taken by the demo camera equip with a C-mount lens show 75% sensitivity enhancement in the λ = 700–1200 nm wavelength range with negligible spatial resolution degradation. Light trapping CIS pixel technology promises to improve NIR sensitivity and appears to be applicable to many different image sensor applications including security camera, personal authentication, and range finding Time-of-Flight camera with IR illuminations.
The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations
(1999)

This paper presents a model to predict the convergence quality of genetic algorithms based on the size of the population. The model is based on an analogy between selection in GAs and one-dimensional random walks. Using the solution to a classic random walk problemthe gambler's ruinthe model naturally incorporates previous knowledge about the initial supply of building blocks (BBs) and correct selection of the best BB over its competitors. The result is an equation that relates the size of the population with the desired quality of the solution, as well as the problem size and difficulty. The accuracy of the model is verified with experiments using additively decomposable functions of varying difficulty. The paper demonstrates how to adjust the model to account for noise present in the fitness evaluation and for different tournament sizes.
Deep Learning Approaches to Semantic Relevance Modeling for Chinese Question-Answer Pairs
(2011)

The human-generated question-answer pairs in the Web social communities are of great value for the research of automatic question-answering technique. Due to the large amount of noise information involved in such corpora, it is still a problem to detect the answers even though the questions are exactly located. Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. Since both the questions and their answers usually contain a small number of sentences, the relevance modeling methods have to overcome the problem of word feature sparsity. In this article, the deep learning principle is introduced to address the semantic relevance modeling task. Two deep belief networks with different architectures are proposed by us to model the semantic relevance for the question-answer pairs. According to the investigation of the textual similarity between the community-driven question-answering (cQA) dataset and the forum dataset, a learning strategy is adopted to promote our models’ performance on the social community corpora without hand-annotating work. The experimental results show that our method outperforms the traditional approaches on both the cQA and the forum corpora.
JINS MEME algorithm for estimation and tracking of concentration of users
(2017)

Activity tracking using a wearable device is an emerging research field. Large-scale studies on activity tracking performed with eyewear-type wearable devices remains a challenging area owing to the negative effect such devices have on users' looks. To cope with this challenge, JINS Inc., an eyewear retailer in Japan, has developed a state-of-the-art smart eyewear called JINS MEME. The design of JINS MEME is almost the same as that of Wellington-type eyeglasses so that people can use it in their daily lives. JINS MEME is equipped with sensors to detect a user's eye movement and head motion. In addition to these functions of JINS MEME, JINS developed an application to measure concentration levels of users. In this demonstration, users will experience wearing the JINS MEME glasses and their concentration will be measured while they perform a certain task at our booth.
A 5GS/s voltage-to-time converter in 90nm CMOS
(2009)

A voltage-to-time converter (VTC) is presented for use in a time-based analog-to-digital converter (ADC). The converter runs with a 5 GHz input clock to provide a maximum conversion rate of 5 GS/s. A novel architecture enables the VTC to provide an adjustable linear delay versus input voltage characteristic. The circuit is realized in a 90nm CMOS process. After calibration, the converter achieves better than 3.7 effective bits for input frequencies up to 1.75 GHz, making it suitable for use in a time-based ADC with up to 4-bit resolution.
A Client-Driven Approach for Channel Management in Wireless LANs
(2006)

We propose an efficient client-based approach for channel management (channel assignment and load balancing) in 802.11-based WLANs that lead to better usage of the wireless spectrum. This approach is based on a “conflict set coloring” formulation that jointly performs load balancing along with channel assignment. Such a formulation has a number of advantages. First, it explicitly captures interference effects at clients. Next, it intrinsically exposes opportunities for better channel re-use. Finally, algorithms based on this formulation do not depend on specific physical RF models and hence can be applied efficiently to a wide-range of inbuilding as well as outdoor scenarios. We have performed extensive packet-level simulations and measurements on a deployed wireless testbed of 70 APs to validate the performance of our proposed algorithms. We show that in addition to single network scenarios, the conflict set coloring formulation is well suited for channel assignment where multiple wireless networks share and contend for spectrum in the same physical space. Our results over a wide range of both simulated topologies and in-building testbed experiments indicate that our approach improves application level performance at the clients by upto three times (and atleast 50%) in comparison to current best-known techniques.
Meta-Learning with Hessian Free Approach in Deep Neural Nets Training
(2018)

Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years. But most of the current methods are still not capable to train complex neuron net model with long-time training process. In this paper, a novel second-order meta-optimizer, named Meta-learning with Hessian-Free(MLHF) approach, is proposed based on the Hessian-Free approach. Two recurrent neural networks are established to generate the damping and the precondition matrix of this Hessian-Free framework. A series of techniques to meta-train the MLHF towards stable and reinforce the meta-training of this optimizer, including the gradient calculation of H . Numerical experiments on deep convolution neural nets, including CUDA-convnet and ResNet18(v2), with datasets of CIFAR10 and ILSVRC2012, indicate that the MLHF shows good and continuous training performance during the whole long-time training process, i.e., both the rapiddecreasing early stage and the steadily-deceasing later stage, and so is a promising meta-learning framework towards elevating the training efficiency in real-world deep neural nets.
Thoth: Comprehensive Policy Compliance in Data Retrieval Systems
(2016)

Data retrieval systems process data from many sources, each subject to its own data use policy. Ensuring compliance with these policies despite bugs, misconfiguration, or operator error in a large, complex, and fast evolving system is a major challenge. Thoth provides an efficient, kernel-level compliance layer for data use policies. Declarative policies are attached to the systems’ input and output files, key-value tuples, and network connections, and specify the data’s integrity and confidentiality requirements. Thoth tracks the flow of data through the system, and enforces policy regardless of bugs, misconfigurations, compromises in application code, or actions by unprivileged operators. Thoth requires minimal changes to an existing system and has modest overhead, as we show using a prototype Thoth-enabled data retrieval system based on the popular Apache Lucene.
A multiparadigm intelligent tutoring system for robotic arm training
(2013)

To assist learners during problem-solving activities, an intelligent tutoring system (ITS) has to be equipped with domain knowledge that can support appropriate tutoring services. Providing domain knowledge is usually done by adopting one of the following paradigms: building a cognitive model, specifying constraints, integrating an expert system, and using data mining algorithms to learn domain knowledge. However, for some ill-defined domains, each single paradigm may present some advantages and limitations in terms of the required resources for deploying it, and tutoring support that can be offered. To address this issue, we propose using a multiparadigm approach. In this paper, we explain how we have applied this idea in CanadarmTutor, an ITS for learning to operate the Canadarm2 robotic arm. To support tutoring services in this ill-defined domain, we have developed a multiparadigm model combining: 1) a cognitive model to cover well-defined parts of the task and spatial reasoning, 2) a data mining approach for automatically building a task model from user solutions for ill-defined parts of the task, and 3) a 3D path-planner to cover other parts of the task for which no user data are available. The multiparadigm version of CanadarmTutor allows providing a richer set of tutoring services than what could be offered with previous single paradigm versions of CanadarmTutor.
Intellectual Capital (IC) Measurement in the Mass Media Context
(2012)

Mass media is the key in°uencer of public opinion. The in°uence is not only limited to political and social, but also relates to organisational and economical reputation and brands. Within public opinion, organisations must manage how they are represented competitively within mass media so that they can develop their brand strategically to grow and compete in the current global knowledge economy. This is where the link to Intellectual Capital (IC) Measurement is signi ̄cant. IC, as the sum of all an organisation's intangible assets drives a company's presence and value within the media, albeit related to human, structural or relational capital attributes. The measurement, therefore, of IC in the mass media context is invaluable to understand how a company is placed strategically and competitively in the external space, and how this links to internal activities, goals and outcomes. This paper is an attempt to address some of the issues related to IC measurements in the mass media context by suggesting a framework that provides a multidisciplinary and holistic approach to the understanding and contextualising of the organisation's presence in the public space.
Orthogonal nonnegative matrix tri-factorization for co-clustering: Multiplicative updates on Stiefel manifolds
(2010)

Matrix factorization-based methods become popular in dyadic data analysis, where a fundamental problem, for example, is to perform document clustering or co-clustering words and documents given a term-document matrix. Nonnegative matrix tri-factorization (NMTF) emerges as a promising tool for co-clustering, seeking a 3-factor decomposition X USV with all factor matrices restricted to be nonnegative, i.e., U P 0; S P 0;V P 0: In this paper we develop multiplicative updates for orthogonal NMTF where X USV is pursued with orthogonality constraints, UU 1⁄4 I; and VV 1⁄4 I, exploiting true gradients on Stiefel manifolds. Experiments on various document data sets demonstrate that our method works well for document clustering and is useful in revealing polysemous words via co-clustering words and documents. 2010 Elsevier Ltd. All rights reserved.
Metal Additive Manufacturing : A Review
(2014)

This paper reviews the state-of-the-art of an important, rapidly emerging, manufacturing technology that is alternatively called additive manufacturing (AM), direct digital manufacturing, free form fabrication, or 3D printing, etc. A broad contextual overview of metallic AM is provided. AM has the potential to revolutionize the global parts manufacturing and logistics landscape. It enables distributed manufacturing and the productions of parts-on-demand while offering the potential to reduce cost, energy consumption, and carbon footprint. This paper explores the material science, processes, and business consideration associated with achieving these performance gains. It is concluded that a paradigm shift is required in order to fully exploit AM potential.
Measurement Science Needs for Real-time Control of Additive Manufacturing Powder Bed Fusion Processes
(2015)

Additive Manufacturing is increasingly used in the development of new products: from conceptual design to functional parts and tooling. However, today, variability in part quality due to inadequate dimensional tolerances, surface roughness, and defects, limits its broader acceptance for high-value or mission-critical applications. While process control in general can limit this variability, it is impeded by a lack of adequate process measurement methods. Process control today is based on heuristics and experimental data, yielding limited improvement in part quality. The overall goal is to develop the measurement science 1 necessary to make in-process measurement and real-time control possible in additive manufacturing. Traceable dimensional and thermal metrology methods must be developed for real-time closed-loop control of additive manufacturing processes. As a precursor, this report presents a review on the additive manufacturing control schemes, process measurements, and modeling and simulation methods as it applies to the powder bed fusion process, though results from other processes are reviewed where applicable. The aim of the review is to identify and summarize the measurement science needs that are critical to real-time process control. We organize our research findings to identify the correlations between process parameters, process signatures, and product quality. The intention of this report is to serve as a background reference and a go-to place for our work to identify the most suitable measurement methods and corresponding measurands for real-time control. 1 Measurement science broadly includes: development of performance metrics, measurement and testing methods, predictive modeling and simulation tools, knowledge modeling, protocols, technical data, and reference materials and artifacts; conduct of inter-comparison studies and calibrations; evaluation of technologies, systems, and practices, including uncertainty analysis; development of the technical basis for standards, codes, and practices in many instances via test-beds, consortia, standards and codes development organizations, and/or other partnerships with industry and academia.
A Tlreshold Selection Method from Gray-Level Histograms
(2008)

A nonparametric and unsupervised method ofautomatic threshold selection for picture segmentation is presented. An optimal threshold is selected by the discriminant criterion, namely, so as to maximize the separability of the resultant classes in gray levels. The procedure is very simple, utilizing only the zerothand the first-order cumulative moments of the gray-level histogram. It is straightforward to extend the method to multithreshold problems. Several experimental results are also presented to support the validity of the method.
Additive manufacturing of polymer-derived ceramics
(2016)

The extremely high melting point of many ceramics adds challenges to additive manufacturing as compared with metals and polymers. Because ceramics cannot be cast or machined easily, three-dimensional (3D) printing enables a big leap in geometrical flexibility. We report preceramic monomers that are cured with ultraviolet light in a stereolithography 3D printer or through a patterned mask, forming 3D polymer structures that can have complex shape and cellular architecture. These polymer structures can be pyrolyzed to a ceramic with uniform shrinkage and virtually no porosity. Silicon oxycarbide microlattice and honeycomb cellular materials fabricated with this approach exhibit higher strength than ceramic foams of similar density. Additive manufacturing of such materials is of interest for propulsion components, thermal protection systems, porous burners, microelectromechanical systems, and electronic device packaging.
Additive Manufacturing of Metals: A Review
(2011)

Over the past 20 years, additive manufacturing (AM) has evolved from 3D printers used for rapid prototyping to sophisticated rapid manufacturing that can create parts directly without the use of tooling. AM technologies build near net shape components layer by layer using 3D model data. AM could revolutionize many sectors of manufacturing by reducing component lead time, material waste, energy usage, and carbon footprint. Furthermore, AM has the potential to enable novel product designs that could not be fabricated using conventional processes. This proceedings is a review that assesses available AM technologies for direct metal fabrication. Included is an outline of the definition of AM, a review of the commercially available and under development technologies for direct metal fabrication, possibilities, and an overall assessment of the state of the art. Perspective on future research opportunities is also be presented.
Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription
(2011)

We investigate the potential of Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, from a feature-engineering perspective. Recently, we had shown that for speaker-independent transcription of phone calls (NIST RT03S Fisher data), CD-DNN-HMMs reduced the word error rate by as much as one third—from 27.4%, obtained by discriminatively trained Gaussian-mixture HMMs with HLDA features, to 18.5%—using 300+ hours of training data (Switchboard), 9000+ tied triphone states, and up to 9 hidden network layers.
An Exploratory Review of Design Principles in Constructivist Gaming Learning Environments.
(2009)

Creating a design theory for Constructivist Gaming Learning Environment necessitates, among other things, the establishment of design principles. These principles have the potential to help designers produce games, where users achieve higher levels of learning. This paper focuses on twelve design principles: Probing, Distributed, Multiple Routes, Practice, Psychosocial Moratorium, Regime of Competence, Self-Knowledge, Collective Knowledge, Engaging, User Interface Ease of Use, On Demand and Just-in-Time Tutorial, and Achievement. We report on two pilot studies of a qualitative nature in which we test our design principles. Game play testing and observations were carried out on five Massively Multiplayer Online Games (MMOGs): RuneScape, GuildWars, Ragnarok, World of WarCraft, and Final Fantasy XI. Two educational games, Carabella Goes to College and Outbreak at WatersEdge were also observed. Our findings indicate that not all of the popular MMOGs and educational games support all of these principles.